Education

• Ph.D., Philosophy, Carnegie Mellon University, 2013
• M.S., Logic, Computation and Methodology, Carnegie Mellon University, 2010
• M.A., Philosophy, University College London, 2007
• B.A., Physics, National Taiwan University, 2003

About

Hanti Lin is a philosopher of science and formal epistemologist, with papers published in philosophy as well as theoretical computer science. Before he joined UC Davis, he was a postdoc at the Australian National University.

Research Focus

In the the past few years Hanti Lin has been working on a project that aims to justify certain kinds of inductive inferences and make some progress in our endeavor to reply to Hume's problem of induction. To set the bar very high, the project targets specifically at inductive inferences that are fundamental to the sciences but have hitherto resisted any justification to any extent in statistics, machine learning theory, or formal epistemology.

Publications

• Lin, H. (forthcoming) Modes of Convergence to the Truth: Steps toward a Better Epistemology of Induction, the Review of Symbolic Logic.

   

This paper aims to justify enumerative induction in its full version---a task that very few formal epistemologists (if any) have attempted before.

• Lin, H. & Zhang, J. (2020) On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption, the Proceedings of Machine Learning Research.

This is a paper in statistics and machine learning theory, proving the theorems that are needed for the philosophical purpose of the paper below.

• Lin (2019) The Hard Problem of Theory Choice: A Case Study on Causal Inference and Its Faithfulness Assumption, Philosophy of Science.

With the same justification strategy as in the preceding paper, this paper aims to justify causal inference without assuming what almost all theorists of causal discovery assume: the (in)famous Causal Faithfulness Condition or the like.

Teaching

Hanti Lin usually teaches “Theory of Knowledge,” “Formal Epistemology,” “Logic, Probability, and Artificial Intelligence,” or some other courses that concern the rationality or justification of inquiry or decision-making.